and for MAM providers AI is a game changer .
AI boosts the efficiency of computers by analyzing audience demands , managing data and filtering content for specific themes , all of which is very appealing to broadcasters . It also supports the creation of more , original and personalized content for increased viewer and fan engagement , reduces production costs and shortens the path toward monetization . to maintain their profitability .
So how do media companies add value for their customers and ensure that AI is a genuine solution ? The answer is an investment in technology that efficiently automates common , repetitive tasks . Automation tools that leverage AI will help production teams produce more and personalized content that allows media companies to gain fans , viewer engagement and increased revenues .
During the live game , the program feed is passed through a computer vision AI engine and a speech-to-text AI engine , which provide additional metadata and contextual information about the game to augment media logs .
Combined , these different technologies enable automatic logging of a live event , and generation of event logs during the game . This supports Tier2 federations or smaller broadcasters , and clubs who do not have funds to buy the service of an external data provider . It also allows companies still manually logging the game to optimize their resources .
Industry leaders will recognize that AI and machine learning can be leveraged to extend the reach of their products . A series of different applications are about to be introduced that will focus on specific functions , but most will fall into the categories of increased metadata generation and its application , media augmentation and annotation , or machine learning applied to workflow operations to further automation .
Companies such as Amazon , Facebook and Twitter , new comers to the digital media economy , have triggered an increase in sports rights costs and posed a new challenge for broadcasters who need to reduce their production costs
Sport production is demanding , it ’ s all about live content and speed , and is the perfect illustration how AI combined with MAM can help media companies face today ’ s challenges .
Assembling sports highlights is a prime example , broadcasters can automatically pre-configure events in the MAM to drastically reduce preparation time and avoid human errors . The question is , how do MAM providers create sophisticated AI options for broadcasters and media companies that enable these efficiencies ? An AI powered metadata engine to fully automate in-game production is key .
AI will also assist in automatic content tagging , which is traditionally a labor heavy and expensive process . Enhanced MAM Search capability opens the door to more content creation through the use of historical archive material to enhance live broadcasts , thereby increasing monetization opportunities .
Together , these technologies enable automatic highlights creation with the best IN and OUT point calculated by the AI engines . Clips and EDLs are automatically created and automatically published to social networks , efficiently increasing the amount of content created with the same staff . Tightly integrated with AI tools , an advanced MAM can automatically generate an increased number of highlight clips during or after an event and deliver this advanced story-telling to a very targeted audience increasing the potential for significant growth in fan engagement while reducing production costs .
90 • Broadcast Beat Magazine • www . broadcastbeat . com
and for MAM providers AI is a
game changer.
AI boosts the efficiency of
computers by analyzing audi-
ence demands, managing data
and filtering content for spe-
cific themes, all of which is very
appealing to broadcasters. It
also supports the creation of
more, original and personalized
content for increased viewer
and fan engagement, reduces
production costs and shortens
the path toward monetization.
Industry leaders will recognize
that AI and machine learning
can be leveraged to extend the
reach of their products. A series
of different applications are
about to be introduced that will
focus on specific functions, but
most will fall into the categories
of increased metadata genera-
tion and its application, media
augmentation and annotation,
or machine learning applied to
workflow operations to further
automation.
Companies such as Amazon,
Facebook and Twitter, new
comers to the digital media
economy, have triggered an
increase in sports rights costs
and posed a new challenge
for broadcasters who need to
reduce their production costs
to maintain their profitability.
So how do media companies
add value for their customers
and ensure that AI is a genu-
ine solution? The answer is an
investment in technology that
efficiently automates common,
repetitive tasks. Automation
tools that leverage AI will help
production teams produce
more and personalized content
that allows media companies to
gain fans, viewer engagement
and increased revenues.
Sport production is demand-
ing, it’s all about live content
and speed, and is the perfect
illustration how AI combined
with MAM can help media com-
panies face today’s challenges.
Assembling sports highlights is
a prime example, broadcasters
can automatically pre-configure
events in the MAM to drasti-
cally reduce preparation time
and avoid human errors. The
question is, how do MAM pro-
viders create sophisticated AI
options for broadcasters and
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